
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter1d
import seaborn as sns
import matplotlib as mpl
from matplotlib.lines import Line2D
import yaml
from scipy import stats

import sys
sys.path.append('../utils/')
from ImagingUtilities import *

import warnings
warnings.filterwarnings('ignore')

from scipy.optimize import curve_fit
from sklearn.preprocessing import StandardScaler

from sklearn.metrics import mean_squared_log_error, r2_score

with open("../data/resources/rcParams.yaml") as f:
    rcParamsDict = yaml.full_load(f)
    for k in rcParamsDict["rcParams"]:
        print("{} {}".format(k,rcParamsDict["rcParams"][k]))
        plt.rcParams[k] = rcParamsDict["rcParams"][k]
    for k1 in set(list(rcParamsDict)).difference(set(["rcParams"])):
        print("{} {}".format(k1,rcParamsDict[k1]))

figure.dpi 80
savefig.dpi 500
figure.figsize [10, 10]
axes.facecolor None
figure.facecolor None
dotSize 20


line_palette = {
 'CTL01A': '#DBB807',
    'CTL08A': '#0FB248',
    'CTL04E': '#FF0054',
    'CTL02A': '#7B00FF',
'H9': '#72190E',
 'H1': '#994F88',
 'CTL05A': '#1965B0',
 'CTL07C': '#437DBF',
 'CTL06F': '#CAE0AB',
 'CTL09A': '#FFFF00',
 'KTD8.2': '#E65518',
 'UCSFi001-A': '#7BAFDE'}


total_df = pd.read_csv('../../iPSC_imaging/quantifications/quantification.csv', index_col=0)
add_tp = pd.read_csv('../../iPSC_imaging/quantifications/quantification_addTP.csv', index_col=0)
total_df[ ~ total_df.duplicated()]
total_df = pd.concat([total_df, add_tp])

donor_map_names = {i:j for i, j in zip(total_df['line'], total_df['line'])}
donor_map_names['CHD2WT'] = 'UCSFi001-A'
donor_map_names['CHD8WT'] = 'H9'
total_df['line'] = total_df['line'].map(donor_map_names)
total_df.shape

(2776, 16)


total_df['pixel_size'] = 1.38


total_df['Area (microm2)'] = total_df.total_area * 1.38
total_df['Area (mm2)'] = total_df['Area (microm2)'] / 1000


total_df['line'].unique()

array(['H1', 'CTL04E', 'CTL02A', 'CTL05A', 'H9', 'KTD8.2', 'CTL09A',
       'CTL06F', 'CTL08A', 'CTL07C', 'UCSFi001-A', 'CTL01A'], dtype=object)


all_tp = total_df.time_point.unique()
all_tp.sort()
all_tp

array(['01_11_23_t18', '02_11_23_t18', '02_11_23_t9', '03_11_23_t18',
       '03_11_23_t9', '04_11_23_t10', '04_11_23_t18', '05_11_23_t18',
       '05_11_23_t9', '06_11_23_t18', '06_11_23_t9', '07_11_23_t18',
       '07_11_23_t9', '08_11_23_t18', '08_11_23_t9', '09_11_23_t20',
       '09_11_23_t9', '10_11_23_t18', '10_11_23_t9', '11_11_23_t18',
       '11_11_23_t9', '12_11_23_t13', '12_11_23_t18', '13_11_23_t18',
       '13_11_23_t9', '14_11_23_t17', '14_11_23_t9', '15_11_23_t17',
       '15_11_23_t9', '16_11_23_t10', '16_11_23_t18', '17_11_23_t17',
       '17_11_23_t9', '18_11_23_t10', '18_11_23_t18', '19_11_23_t10',
       '19_11_23_t18', '20_11_23_t18', '20_11_23_t9', '21_11_23_t17',
       '21_11_23_t9', '22_11_23_t10', '31_10_23_t18', '31_10_23_t9'],
      dtype=object)


total_df['line_split'] = total_df['line'].astype('str') + '_' + total_df['split_time'].astype('str')

mean_df_time_point = total_df.groupby(['split_time']).mean('perc_area')
mean_df_time_point_dict = {i:j for i, j in zip(mean_df_time_point.index, mean_df_time_point.perc_area)}
mean_df_time_point_dict

area_df_time_point = total_df.groupby(['line','split_time']).mean('Area (microm2)').reset_index()
area_df_time_point['line_split'] = area_df_time_point['line'].astype('str') + '_' + area_df_time_point['split_time'].astype('str')
area_df_time_point = {i:j for i, j in zip(area_df_time_point.line_split, area_df_time_point['Area (microm2)'])}
area_df_time_point

total_df['norm_factor'] = total_df.split_time.map(mean_df_time_point_dict)
total_df['perc_area_norm'] = total_df['perc_area'] / total_df['norm_factor']

total_df['mean_area_tp'] = total_df.line_split.map(area_df_time_point)
total_df['area_error'] = (total_df['mean_area_tp'] - total_df['Area (microm2)']) / total_df['Area (microm2)']
total_df['std'] = np.sqrt((total_df['Area (microm2)'] - total_df['mean_area_tp'])**2 / len(total_df))
total_df['cv'] = total_df['std'] / total_df['mean_area_tp']


total_df['line_n_split'] = total_df['line'] + '_' + total_df['n_split'].astype('str')


sns.kdeplot(total_df['cv'])

<Axes: xlabel='cv', ylabel='Density'>


ax = sns.kdeplot(total_df['area_error'])
ax.axvline(15)

<matplotlib.lines.Line2D at 0x7f22cbcf05e0>


ax = sns.kdeplot(total_df['area_error'])
ax.set_xlim(-1, 30)
ax.axvline(10)

<matplotlib.lines.Line2D at 0x7f22c7bbb970>


fig, ax = plt.subplots(figsize = (20, 10))
sns.scatterplot(data = total_df, y = 'perc_area', x = 'split_time', ax = ax, hue = 'line', palette=line_palette)

<Axes: xlabel='split_time', ylabel='perc_area'>


total_df = total_df[~((total_df['split_time'] < 25) & (total_df['perc_area'] > 10))]


ax = sns.kdeplot(np.log10(total_df['area_error']))


total_df = total_df[total_df['area_error'] < 5]
total_df.shape

(2170, 25)


idx_max = total_df.groupby('line')['n_split'].idxmax()

# Filter the DataFrame using these indices
filtered_df = total_df.drop(idx_max)
filtered_df


total_df['logArea'] = np.log10(total_df['Area (microm2)'] + 0.000001)


order = total_df.line.unique().tolist()
order.sort()


sns.set_theme(style="ticks")

# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(total_df.sort_values(by = 'split_time'), col="line", hue = 'line', palette=line_palette,
                     col_wrap=4, height=5, col_order = order)


# Draw a line plot to show the trajectory of each random walk
grid.map(sns.regplot, "split_time", "Area (mm2)", order = 3)

grid.set_axis_labels("Time point post split", "Area (mm2)")

# Adjust the arrangement of the plots
grid.fig.tight_layout(w_pad=1)


sns.set_theme(style="ticks")

# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(total_df.sort_values(by = 'split_time'), col="line", hue = 'line', palette=line_palette,
                     col_wrap=4, height=5, col_order = order)

# Draw a line plot to show the trajectory of each random walk
grid.map(sns.lineplot, "split_time", "Area (mm2)", markers = True)

grid.set_axis_labels("Time point post split", "Total Area (pixels)")

# Adjust the arrangement of the plots
grid.fig.tight_layout(w_pad=1)


fig, ax = plt.subplots(4, 3, figsize=(30, 21), gridspec_kw={'hspace': 0.7})
ax = ax.flatten()

for ax_index, line in enumerate(total_df.line.unique()):
    sub = total_df[(total_df.line == line) & (total_df.n_split != 'day')].sort_values(by='datetime')
    mean_st = sub.groupby('split_time')['Area (mm2)'].mean()
    std_st = sub.groupby('split_time')['Area (mm2)'].std()
    y_pos = mean_st.index
    ydata = sub['Area (microm2)'].values
    xdata = sub.split_time.values.astype('int')
    farray = mean_st.values

    # Smoothing
    farray_smooth = gaussian_filter1d(farray, sigma=3)

    # Error formatting
    upper_err = gaussian_filter1d(farray + (std_st / 2).values, sigma=3)
    lower_err = gaussian_filter1d(farray - (std_st / 2).values, sigma=3)

    ax[ax_index].scatter(xdata, ydata)
    ax[ax_index].plot(y_pos, farray, '--', linewidth=0.7, color='k', alpha=0.45)
    ax[ax_index].plot(y_pos, farray_smooth, color='#2374AB')
    ax[ax_index].fill_between(y_pos, upper_err, lower_err, color='crimson', alpha=0.2)

    # Use meaningful limits for better visualization
    ax[ax_index].set_ylim(0, np.max(farray) * 1.25)

    # Rotate x-axis labels for better readability
    ax[ax_index].tick_params(axis='x', rotation=90)

    # Set axis labels and title
    ax[ax_index].set_title(f'Line {line}')
    ax[ax_index].set_ylabel('Total area')
    ax[ax_index].set_xlabel('Time point after split')

plt.tight_layout()  # Adjust layout to prevent overlapping
plt.show()


sns.set_theme(style="ticks")

split_palette = {'1': '#264653', '2': '#2a9d8f', '3': '#8ab17d', '4': '#e9c46a', '5': '#f4a261', '6': '#e76f51'}
line_split_palette = {}

for i in total_df.line_n_split.unique():
    split_n = i.split('_')[-1]
    line_split_palette[i] = split_palette[split_n]

custom_handles = [Line2D([0], [0], color=color, lw=2) for color in split_palette.values()]
grid = sns.FacetGrid(total_df.sort_values(by = 'split_time'), col="line", hue = 'line_n_split', palette=line_split_palette,
                     col_wrap=4, height=5, col_order = order)
grid.map(sns.lineplot, "split_time", "Area (mm2)", markers = True)

grid.add_legend()
if grid._legend:
    grid._legend.remove()  

legend = grid.fig.legend(custom_handles, split_palette.keys(), ncol=2, frameon=False, bbox_to_anchor = (1.2,1), fontsize = 25)

legend.set_title('Passage number', prop={'size': 30})

for ax in grid.axes.flat:
    ax.set_title(ax.get_title(), fontsize=35)

# Increase the size of x and y tick labels
for ax in grid.axes.flat:
    ax.tick_params(axis='x', labelsize=20)
    ax.tick_params(axis='y', labelsize=20)

grid.set_axis_labels("Hours after splitting", "Total area (mm2)", fontsize = 25)
grid.fig.tight_layout(w_pad=1)
grid.fig.savefig('./figures/raw_GC_iPSC_dividedSplit.svg', dpi = 300, bbox_inches = 'tight')


sns.set_theme(style="ticks")

boolean_sel = pd.Series(total_df.groupby(['line_n_split'])['split_time'].count() > 5)
boolean_sel = boolean_sel[boolean_sel]
filtered_total = total_df[total_df.line_n_split.isin(boolean_sel.index)]

# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(filtered_total.sort_values(by = 'split_time'), col="line_n_split", hue = 'line', palette=line_palette,
                     col_wrap=4, height=5, col_order = order)

grid.map(sns.regplot, "split_time", "Area (microm2)", order = 3)

# Adjust the arrangement of the plots
grid.fig.tight_layout(w_pad=1)


fig, ax = plt.subplots(9,5, figsize = (5*10, 7*9), gridspec_kw={'hspace': 0.7})
ax = ax.flatten().T
ax_index = 0
summary_dfs_dict = {}

for l in order:

    sub = total_df[total_df.line == l]
    sub = sub.sort_values(by = 'datetime')
    sub = sub[sub.n_split != 'day']

    splits = sub.n_split.unique().tolist()
    splits.sort()

    for split in splits:

        subsub = sub[sub.n_split == split]

        if len(subsub.split_time.unique()) > 5:
            
            ydata = subsub['Area (microm2)'].values
            xdata = subsub.split_time.values.astype('int')
    
            
            # user defined function,
            # with `area_sum` return the sum of the areas of all the field of view captured for that line at that time point
            summary_df = preprocess(subsub, original_v='Area (microm2)', final_output='mean')

            summary_dfs_dict[f'{l}_split_{split}'] = summary_df
            
            farray = np.array(summary_df['mean'])
    
            y_pos = summary_df.split_time.values
    
            # Smoothing
            farray_smooth = gaussian_filter1d(farray, sigma=3)
    
            # Error formatting
            upper_err = gaussian_filter1d(farray + (summary_df['stds'] / 2), sigma=3)
            lower_err = gaussian_filter1d(farray - (summary_df['stds'] / 2), sigma=3)
    
            ax[ax_index].scatter(xdata, ydata)
            ax[ax_index].plot(y_pos, farray, '--', linewidth=0.7, color='k', alpha=0.45)
            ax[ax_index].plot(y_pos, farray_smooth, color = '#2374AB')
            ax[ax_index].fill_between(y_pos, upper_err, lower_err, color='crimson', alpha=0.2)
            #ax[ax_index].errorbar(y_pos, farray, yerr=summary_df['stds'], fmt='none', color='crimson', alpha=0.5)
            #
            ax[ax_index].set_ylim(0, np.max(farray)+(np.max((farray)*25)/100))
            ax[ax_index].xaxis.set_tick_params(rotation=90)
            ax[ax_index].set_title(f'{l}_split_{split}')
            ax[ax_index].set_ylabel('Total area')
            ax[ax_index].set_xlabel('Time point')
            ax_index += 1
            
        else:
            print(f'Skipped split {subsub.n_split.values[0]} of line {subsub.line.values[0]}')

plt.show()

Skipped split 3 of line CTL01A
Skipped split 1 of line CTL04E
Skipped split 1 of line CTL09A
Skipped split 1 of line UCSFi001-A


fig, ax = plt.subplots(10,5, figsize = (5*10, 7*9), gridspec_kw={'hspace': 0.7})
ax = ax.flatten().T
ax_index = 0

for l in order:

    sub = total_df[total_df.line == l]
    sub = sub.sort_values(by = 'datetime')
    sub = sub[sub.n_split != 'day']

    splits = sub.n_split.unique().tolist()
    splits.sort()

    for split in splits:

        subsub = sub[sub.n_split == split]

        
        # user defined function,
        # with `area_sum` return the sum of the areas of all the field of view captured for that line at that time point
        summary_df = preprocess(subsub, original_v='logArea', final_output='area_sum')
        
        farray = np.array(summary_df['area_sum'])

        y_pos = summary_df.datetime.values

        # Smoothing
        farray_smooth = gaussian_filter1d(farray, sigma=1)

        # Error formatting
        upper_err = gaussian_filter1d(farray + (summary_df['stds'] / 2), sigma=1)
        lower_err = gaussian_filter1d(farray - (summary_df['stds'] / 2), sigma=1)

        ax[ax_index].plot(y_pos, farray, '--', linewidth=0.7, color='k', alpha=0.45)
        ax[ax_index].plot(y_pos, farray_smooth, color = '#2374AB')
        ax[ax_index].fill_between(y_pos, upper_err, lower_err, color='crimson', alpha=0.2)
        #
        ax[ax_index].set_ylim(0, np.max(farray)+(np.max((farray)*25)/100))
        ax[ax_index].xaxis.set_tick_params(rotation=90)
        ax[ax_index].set_title(f'{l}_split_{split}')
        ax[ax_index].set_ylabel('Log total area')
        ax[ax_index].set_xlabel('Time point')
        ax_index += 1

plt.show()


lines = total_df.line.unique()


discrete_deriv_curves = {}

fig, ax = plt.subplots(10,5, figsize = (5*10, 7*9), gridspec_kw={'hspace': 0.7})
ax = ax.flatten().T
ax_index = 0

for l in total_df.line.unique():
    
    #color = color_dict[l]

    sub = total_df[total_df.line == l]
    sub = sub.sort_values(by = 'datetime')
    sub = sub[sub.n_split != 'day']

    for split in sub.n_split.unique():

        subsub = sub[sub.n_split == split]

        # user defined function,
        # with `area_sum` return the sum of the areas of all the field of view captured for that line at that time point
        summary_df = preprocess(subsub, original_v='Area (microm2)', final_output='mean')
        
        summary_df = summary_df.sort_values('split_time')
        
        summary_df['smoothed'] = gaussian_filter1d(summary_df['mean'], 3)
        summary_df['derivative'] = summary_df['smoothed'].diff() / summary_df['split_time'].diff()

        discrete_deriv_curves[f'{l}_split_{split}'] = summary_df
        
        farray = np.array(summary_df['derivative'])
        
        y_pos = summary_df.split_time.values
        
        ax[ax_index].errorbar(y_pos, farray, marker = 'o')
        
        ax[ax_index]

        ax[ax_index].xaxis.set_tick_params(rotation=90)
        ax[ax_index].set_title(f'{l}_split_{split}')
        ax[ax_index].set_ylabel('Discrete derivative')
        ax[ax_index].set_xlabel('Time point')
        ax_index += 1
        

plt.show()


deriv_df = pd.concat(discrete_deriv_curves.values(), keys = discrete_deriv_curves.keys()).reset_index()
deriv_df['line'] = deriv_df['level_0'].apply(lambda x: x.split('_')[0])
deriv_df['split'] = deriv_df['level_0'].apply(lambda x: x.split('_')[-1])


fig, ax = plt.subplots(4,3, figsize = (5*4, 7*3), gridspec_kw={'hspace': 0.7})
ax = ax.flatten().T
ax_index = 0

for line in deriv_df.line.unique():
    
    #color = color_dict[l]

    sub = deriv_df[deriv_df.line == line]
    
    sub = sub.sort_values('split_time')

    sub['smoothed'] = gaussian_filter1d(sub['derivative'], sigma = 3)

    sns.lineplot(data = sub, y = 'smoothed', x = 'split_time', hue = 'split', ax = ax[ax_index], errorbar='sd', markers = True, palette=split_palette)
    ax[ax_index].xaxis.set_tick_params(rotation=90)
    ax[ax_index].set_title(f'{line}')
    ax[ax_index].set_ylabel('Discrete derivative')
    ax[ax_index].set_xlabel('Time point')
    ax_index += 1


fig, ax = plt.subplots(4,3, figsize = (5*4, 7*3)) #, gridspec_kw={'hspace': 0.7})
ax = ax.flatten().T
ax_index = 0

for line in deriv_df.line.unique():
    
    #color = color_dict[l]

    sub = deriv_df[deriv_df.line == line]
    
    sub = sub.sort_values('split_time')

    sub['smoothed'] = gaussian_filter1d(sub['derivative'], sigma = 3)

    sns.lineplot(data = sub, y = 'smoothed', x = 'split_time', ax = ax[ax_index], errorbar='sd', markers = True, err_style = 'bars')
    ax[ax_index].xaxis.set_tick_params(rotation=90)
    ax[ax_index].set_title(f'{line}')
    ax[ax_index].set_ylabel('Discrete derivative')
    ax[ax_index].set_xlabel('Time point')
    ax_index += 1
    
plt.tight_layout()


fig, ax = plt.subplots(9,5, figsize = (5*10, 7*9))
ax = ax.flatten().T
ax_index = 0

order_line_n = total_df.line_n_split.unique().tolist()
order_line_n.sort()

for line_n in order_line_n:
    
    sub = total_df[total_df.line_n_split == line_n]
    #sub = sub[~sub.index.duplicated()]
    sub = sub.sort_values('split_time')

    if len(sub.split_time.unique()) > 5:
        
        #sub['smoothed'] = sub.groupby('line_n_split')['Area (microm2)'].apply(gaussian_filter1d, sigma = 3).loc[line_n]
        y = sub.groupby('split_time')['Area (mm2)'].mean().cumsum().values
        x = sub.split_time.unique()
        
        sns.lineplot(y = y, x = x, ax = ax[ax_index], errorbar='sd', markers = True, err_style='bars')
        ax[ax_index].xaxis.set_tick_params(rotation=90)
        ax[ax_index].set_title(f'{line_n}')
        ax[ax_index].set_ylabel('Cumulative of mean total area')
        ax[ax_index].set_xlabel('Time point')
        ax_index += 1

plt.tight_layout()


sns.set(style="white", palette="Paired", color_codes=True)
fig, ax = plt.subplots(figsize=(7,5))
labels = []
lc = []
handles = []
all_lines = {}

total_df_no_first = total_df[total_df.n_split != '1'].copy()

for l in total_df.line.unique():
    
    color = line_palette[l]

    sub = total_df[total_df.line == l]
    sub = sub.sort_values(by = 'datetime')
    sub = sub[sub.n_split != 'day']

    for split in sub.n_split.unique():

        subsub = sub[sub.n_split == split]

        # user defined function,
        # with `area_sum` return the sum of the areas of all the field of view captured for that line at that time point
        summary_df = preprocess(subsub, original_v='Area (mm2)', final_output='mean')
        
        summary_df = summary_df.sort_values('split_time')
        
        summary_df['smoothed'] = gaussian_filter1d(summary_df['mean'], 1)
        summary_df['cumulative'] = summary_df['smoothed'].cumsum()
        
        farray = np.array(summary_df['cumulative'])
        
        y_pos = summary_df.split_time.values

        line, = ax.plot(y_pos, farray, color = color, marker = '.')
        ax.xaxis.set_tick_params(rotation=90)
        all_lines[f'{l}_{split}'] = line

    labels.append(l)
    handles.append(line)
    lc.append(color)
    
plt.legend(handles, labels, bbox_to_anchor = (1,1))

<matplotlib.legend.Legend at 0x7f22c4fd7910>


lines = total_df.line.unique()


highlight_growth_curves(all_lines, xlabel = 'Hours from split', ylabel = 'Cumulative growth', lines = lines, fontsize = 20)
#plt.savefig('growth_curve_per_line.pdf', dpi = 300)


len(np.float64(list(range(0,200))))

200


fig, ax = plt.subplots(5,9, figsize = (5*12, 8*5))
ax = ax.flatten().T
ax_index = 0

fitted_param = {}

def exp_model(t, a, b, c):
    return a * np.exp(b * t) + c - a

for line_n in order_line_n:

    fitted_param[line_n] = {}
    sub = total_df[total_df.line_n_split == line_n]
    sub = sub.sort_values('split_time')
    
    if len(sub.split_time.unique()) > 5:
        # Calculate the cumulative sum of the mean total area for each split_time
        y = sub.groupby('split_time')['Area (mm2)'].mean().cumsum().values
        y_min_0 = sub[(sub.split_time == sub.split_time.min()) & (sub['Area (mm2)'] > 0)]['Area (mm2)'].min()

        #print(y_min_0)
            
        
        x = np.array(sub.split_time.unique())
        hue = [line_n.split('_')[0]]*len(y)

        try:
            #print(min(y))
            popt, pcov = curve_fit(exp_model, x, y, p0=(min(y), 0.1, y_min_0), bounds=((-np.inf, 0, y_min_0), (np.inf,np.inf, np.inf)))

            fitted_param[line_n]['a'] = popt[0]
            fitted_param[line_n]['rate'] = popt[1]
            fitted_param[line_n]['min(y)'] = min(y)
            fitted_param[line_n]['c'] = popt[2]
            fitted_param[line_n]['mean_cum'] = y
            fitted_param[line_n]['split_time'] = x
            
            a, b, c = popt
            
            # Generate fitted y values
            y_fitted = exp_model(x, a, b, c)
            y_fitted_viz = exp_model(np.float64(list(range(0, 200))), a, b, c)

            fitted_param[line_n]['y_fitted'] = y_fitted
            fitted_param[line_n]['MSLE'] = mean_squared_log_error(y, y_fitted)
            fitted_param[line_n]['r2'] = r2_score(y, y_fitted)
            
            sns.lineplot(y = y_fitted_viz, x = np.float64(list(range(0, 200))), ax = ax[ax_index]) #, markers = True, hue = hue, palette=line_palette, linewidth = 5, legend = None)
            sns.scatterplot(y = y, x = x, ax = ax[ax_index], markers = True, hue = hue, palette=line_palette, s = 150, legend=None)
            ax[ax_index].xaxis.set_tick_params(rotation=90)
            ax[ax_index].set_title(f'{line_n}', fontsize = 40)
            ax[ax_index].set_ylabel('Cumulative area (mm2)', fontsize = 35)
            ax[ax_index].set_xlabel('Time point', fontsize = 35)
            ax[ax_index].set_xlim(-10, 220)
            ax[ax_index].set_ylim(-10, 85000)
            _ = ax[ax_index].set_xticklabels(ax[ax_index].get_xticklabels(), fontsize = 30)
            _ = ax[ax_index].set_yticklabels(ax[ax_index].get_yticklabels(), fontsize = 30)
            ax_index += 1
        
        except RuntimeError as e:
            print(f"Fitting failed for line_n {line_n}: {e}")
            
plt.tight_layout()
plt.savefig('./figures/Fitted_cum_area_per_split_new_viz.svg', dpi = 300, bbox_inches = 'tight')

Fitting failed for line_n CTL01A_5: Optimal parameters not found: The maximum number of function evaluations is exceeded.


fig, ax = plt.subplots(5,9, figsize = (5*12, 8*5))
ax = ax.flatten().T
ax_index = 0

fitted_param = {}

def exp_model(t, a, b, c):
    return a * np.exp(b * t) + c - a

for line_n in order_line_n:

    fitted_param[line_n] = {}
    sub = total_df[total_df.line_n_split == line_n]
    sub = sub.sort_values('split_time')
    
    if len(sub.split_time.unique()) > 5:
        # Calculate the cumulative sum of the mean total area for each split_time
        y = sub.groupby('split_time')['Area (mm2)'].mean().cumsum().values
        y_min_0 = sub[(sub.split_time == sub.split_time.min()) & (sub['Area (mm2)'] > 0)]['Area (mm2)'].min()

        #print(y_min_0)
            
        
        x = np.array(sub.split_time.unique())
        hue = [line_n.split('_')[0]]*len(y)

        try:
            #print(min(y))
            popt, pcov = curve_fit(exp_model, x, y, p0=(min(y), 0.1, y_min_0), bounds=((-np.inf, 0, y_min_0), (np.inf,np.inf, np.inf)))

            fitted_param[line_n]['a'] = popt[0]
            fitted_param[line_n]['rate'] = popt[1]
            fitted_param[line_n]['min(y)'] = min(y)
            fitted_param[line_n]['c'] = popt[2]
            fitted_param[line_n]['mean_cum'] = y
            fitted_param[line_n]['split_time'] = x
            
            a, b, c = popt
            
            # Generate fitted y values
            y_fitted = exp_model(x, a, b, c)
            y_fitted_viz = exp_model(np.float64(list(range(0, 200))), a, b, c)

            fitted_param[line_n]['y_fitted'] = y_fitted
            #fitted_param[line_n]['MSLE'] = mean_squared_log_error(y, y_fitted)
            fitted_param[line_n]['r2'] = r2_score(y, y_fitted)
            
            sns.lineplot(y = y_fitted, x = x, ax = ax[ax_index], markers = True, hue = hue, palette=line_palette, linewidth = 5, legend = None)
            sns.scatterplot(y = y, x = x, ax = ax[ax_index], markers = True, hue = hue, palette=line_palette, s = 150, legend=None)
            ax[ax_index].xaxis.set_tick_params(rotation=90)
            ax[ax_index].set_title(f'{line_n}', fontsize = 40)
            ax[ax_index].set_ylabel('Cumulative area (mm2)', fontsize = 35)
            ax[ax_index].set_xlabel('Time point', fontsize = 35)
            #ax[ax_index].set_xlim(-10, 220)
            #ax[ax_index].set_ylim(-10, 85000)
            _ = ax[ax_index].set_xticklabels(ax[ax_index].get_xticklabels(), fontsize = 30)
            _ = ax[ax_index].set_yticklabels(ax[ax_index].get_yticklabels(), fontsize = 30)
            ax_index += 1
        
        except RuntimeError as e:
            print(f"Fitting failed for line_n {line_n}: {e}")
            
plt.tight_layout()
plt.savefig('./figures/Fitted_cum_area_per_split_orig_viz.svg', dpi = 300, bbox_inches = 'tight')

Fitting failed for line_n CTL01A_5: Optimal parameters not found: The maximum number of function evaluations is exceeded.


donor_map_names = {i:j for i, j in zip(total_df['line'], total_df['line'])}
donor_map_names['CHD2WT'] = 'UCSFi001-A'
donor_map_names['CHD8WT'] = 'H9'
donor_map_names['CTL01A'] = 'CTL01'
total_df['line'] = total_df['line'].map(donor_map_names)
total_df['line_n_split'] = total_df['line'] + '_' + total_df['n_split'].astype('str')
order_line_n = total_df.line_n_split.unique().tolist()
order_line_n.sort()

line_palette = {'CTL01': '#DBB807',
 'CTL08A': '#0FB248',
 'CTL04E': '#FF0054',
 'CTL02A': '#7B00FF',
 'H9': '#72190E',
 'H1': '#994F88',
 'CTL05A': '#1965B0',
 'CTL07C': '#437DBF',
 'CTL06F': '#CAE0AB',
 'CTL09A': '#FFFF00',
 'KTD8.2': '#E65518',
 'UCSFi001-A': '#7BAFDE'}

fig, ax = plt.subplots(5,9, figsize = (5*12, 8*5))
ax = ax.flatten().T
ax_index = 0

fitted_param = {}

def exp_model(t, a, b, c):
    return a * np.exp(b * t) + c - a

for line_n in order_line_n:

    fitted_param[line_n] = {}
    sub = total_df[total_df.line_n_split == line_n]
    sub = sub.sort_values('split_time')
    
    if len(sub.split_time.unique()) > 5:
        # Calculate the cumulative sum of the mean total area for each split_time
        y = sub.groupby('split_time')['Area (mm2)'].mean().cumsum().values
        y_min_0 = sub[(sub.split_time == sub.split_time.min()) & (sub['Area (mm2)'] > 0)]['Area (mm2)'].min()

        #print(y_min_0)
            
        
        x = np.array(sub.split_time.unique())
        hue = [line_n.split('_')[0]]*len(y)

        try:
            #print(min(y))
            popt, pcov = curve_fit(exp_model, x, y, p0=(min(y), 0.1, y_min_0), bounds=((-np.inf, 0, y_min_0), (np.inf,np.inf, np.inf)))

            fitted_param[line_n]['a'] = popt[0]
            fitted_param[line_n]['rate'] = popt[1]
            fitted_param[line_n]['min(y)'] = min(y)
            fitted_param[line_n]['c'] = popt[2]
            fitted_param[line_n]['mean_cum'] = y
            fitted_param[line_n]['split_time'] = x
            
            a, b, c = popt
            
            # Generate fitted y values
            y_fitted = exp_model(x, a, b, c)
            y_fitted_viz = exp_model(np.float64(list(range(0, 200))), a, b, c)

            fitted_param[line_n]['y_fitted'] = y_fitted
            fitted_param[line_n]['MSLE'] = mean_squared_log_error(y, y_fitted)
            fitted_param[line_n]['r2'] = r2_score(y, y_fitted)
            
            sns.lineplot(y = y_fitted_viz, x = np.float64(list(range(0, 200))), ax = ax[ax_index]) #, markers = True, hue = hue, palette=line_palette, linewidth = 5, legend = None)
            sns.scatterplot(y = y, x = x, ax = ax[ax_index], markers = True, hue = hue, palette=line_palette, s = 150, legend=None)
            ax[ax_index].xaxis.set_tick_params(rotation=90)
            ax[ax_index].set_title(f'{line_n}', fontsize = 40)
            ax[ax_index].set_ylabel('Cumulative area (mm2)', fontsize = 35)
            ax[ax_index].set_xlabel('Time point', fontsize = 35)
            ax[ax_index].set_xlim(-10, 220)
            ax[ax_index].set_ylim(-10, 85000)
            _ = ax[ax_index].set_xticklabels(ax[ax_index].get_xticklabels(), fontsize = 30)
            _ = ax[ax_index].set_yticklabels(ax[ax_index].get_yticklabels(), fontsize = 30)
            ax_index += 1
        
        except RuntimeError as e:
            print(f"Fitting failed for line_n {line_n}: {e}")
            
plt.tight_layout()
plt.savefig('./figures/Fitted_cum_area_per_split_new_viz_new_name.svg', dpi = 300, bbox_inches = 'tight')

Fitting failed for line_n CTL01_5: Optimal parameters not found: The maximum number of function evaluations is exceeded.


donor_map_names = {i:j for i, j in zip(total_df['line'], total_df['line'])}
donor_map_names['CHD2WT'] = 'UCSFi001-A'
donor_map_names['CHD8WT'] = 'H9'
donor_map_names['CTL01'] = 'CTL01A'
total_df['line'] = total_df['line'].map(donor_map_names)
total_df['line_n_split'] = total_df['line'] + '_' + total_df['n_split'].astype('str')
order_line_n = total_df.line_n_split.unique().tolist()
order_line_n.sort()

line_palette = {'CTL01A': '#DBB807',
 'CTL08A': '#0FB248',
 'CTL04E': '#FF0054',
 'CTL02A': '#7B00FF',
 'H9': '#72190E',
 'H1': '#994F88',
 'CTL05A': '#1965B0',
 'CTL07C': '#437DBF',
 'CTL06F': '#CAE0AB',
 'CTL09A': '#FFFF00',
 'KTD8.2': '#E65518',
 'UCSFi001-A': '#7BAFDE'}

fig, ax = plt.subplots(5,9, figsize = (5*12, 8*5))
ax = ax.flatten().T
ax_index = 0

fitted_param = {}

def exp_model(t, a, b, c):
    return a * np.exp(b * t) + c - a

for line_n in order_line_n:

    fitted_param[line_n] = {}
    sub = total_df[total_df.line_n_split == line_n]
    sub = sub.sort_values('split_time')
    
    if len(sub.split_time.unique()) > 5:
        # Calculate the cumulative sum of the mean total area for each split_time
        y = sub.groupby('split_time')['Area (mm2)'].mean().cumsum().values
        y_min_0 = sub[(sub.split_time == sub.split_time.min()) & (sub['Area (mm2)'] > 0)]['Area (mm2)'].min()

        #print(y_min_0)
            
        
        x = np.array(sub.split_time.unique())
        hue = [line_n.split('_')[0]]*len(y)

        try:
            #print(min(y))
            popt, pcov = curve_fit(exp_model, x, y, p0=(min(y), 0.1, y_min_0), bounds=((-np.inf, 0, y_min_0), (np.inf,np.inf, np.inf)))

            fitted_param[line_n]['a'] = popt[0]
            fitted_param[line_n]['rate'] = popt[1]
            fitted_param[line_n]['min(y)'] = min(y)
            fitted_param[line_n]['c'] = popt[2]
            fitted_param[line_n]['mean_cum'] = y
            fitted_param[line_n]['split_time'] = x
            
            a, b, c = popt
            
            # Generate fitted y values
            y_fitted = exp_model(x, a, b, c)
            y_fitted_viz = exp_model(np.float64(list(range(0, 200))), a, b, c)

            fitted_param[line_n]['y_fitted'] = y_fitted
            fitted_param[line_n]['MSLE'] = mean_squared_log_error(y, y_fitted)
            fitted_param[line_n]['r2'] = r2_score(y, y_fitted)
            
            sns.lineplot(y = y_fitted, x = x, ax = ax[ax_index], markers = True, hue = hue, palette=line_palette, linewidth = 5, legend = None)
            sns.scatterplot(y = y, x = x, ax = ax[ax_index], markers = True, hue = hue, palette=line_palette, s = 150, legend=None)
            ax[ax_index].xaxis.set_tick_params(rotation=90)
            ax[ax_index].set_title(f'{line_n}', fontsize = 40)
            ax[ax_index].set_ylabel('Cumulative area (mm2)', fontsize = 35)
            ax[ax_index].set_xlabel('Time point', fontsize = 35)
            #ax[ax_index].set_xlim(-10, 220)
            #ax[ax_index].set_ylim(-10, 85000)
            _ = ax[ax_index].set_xticklabels(ax[ax_index].get_xticklabels(), fontsize = 30)
            _ = ax[ax_index].set_yticklabels(ax[ax_index].get_yticklabels(), fontsize = 30)
            ax_index += 1
        
        except RuntimeError as e:
            print(f"Fitting failed for line_n {line_n}: {e}")
            
plt.tight_layout()
plt.savefig('./figures/Fitted_cum_area_per_split_orig_viz_new_name.svg', dpi = 300, bbox_inches = 'tight')

Fitting failed for line_n CTL01A_5: Optimal parameters not found: The maximum number of function evaluations is exceeded.


sns.set(style="white", palette="Paired", color_codes=True)
fig, ax = plt.subplots(figsize=(7,5))
labels = []
lc = []
handles = []
all_lines = {}

total_df_no_first = total_df[total_df.n_split != '1'].copy()

for l in total_df.line.unique():
    
    color = line_palette[l]

    sub = total_df[total_df.line == l]
    sub = sub.sort_values(by = 'datetime')
    sub = sub[sub.n_split != 'day']

    for split in sub.n_split.unique():


        subsub = sub[sub.n_split == split]

        if len(subsub.split_time.unique()) > 5:

            # user defined function,
            # with `area_sum` return the sum of the areas of all the field of view captured for that line at that time point
            summary_df = preprocess(subsub, original_v='Area (mm2)', final_output='mean')
            
            summary_df = summary_df.sort_values('split_time')
            summary_df['cumulative'] = summary_df['mean'].cumsum()
            
            farray = np.array(summary_df['cumulative'].values)
            y_pos = summary_df.split_time.values
    
            popt, pcov = curve_fit(exp_model, y_pos, farray, p0=(max(farray), 0.1, 0))
            
            a, b, c = popt
    
            y_fitted = exp_model(y_pos, a, b, c)
    
            line, = ax.plot(y_pos, y_fitted, color = color, marker = '.')
            ax.xaxis.set_tick_params(rotation=90)
            all_lines[f'{l}_{split}'] = line
    
        labels.append(l)
        handles.append(line)
        lc.append(color)
    
plt.legend(handles, labels, bbox_to_anchor = (1,1))

<matplotlib.legend.Legend at 0x7f22c79c3d60>


lines = total_df.line.unique()


highlight_growth_curves(all_lines, xlabel = 'Hours from split', ylabel = 'Cumulative area (mm2)', lines = lines, fontsize = 20)
plt.savefig('./figures/cumulative_growth_curve_per_line.svg', dpi = 300)


fitted_df = pd.DataFrame.from_dict(fitted_param).T
fitted_df['Line'] = fitted_df.reset_index()['index'].apply(lambda x: x.split('_')[0]).values
fitted_df['split'] = fitted_df.reset_index()['index'].apply(lambda x: x.split('_')[1]).values
fitted_df = fitted_df[~fitted_df.a.isna()]
fitted_df['a+c'] = fitted_df['a'] + fitted_df['c']


fitted_df[fitted_df['a+c'] > 0].shape

(43, 12)


fitted_df


fitted_df[fitted_df['a+c'] < 0].shape

(0, 12)


fitted_df_filtered = fitted_df[fitted_df['r2'] > 0.9]
fitted_df_filtered


fig, ax = plt.subplots()
sns.barplot(data = fitted_df_filtered, x = 'split', y = 'rate', order = ['1', '2', '3', '4', '5', '6'], ax = ax)#, hue = 'Line', palette=line_palette)
_ = ax.set_ylabel('Growth rate', fontsize = 20)
_ = ax.set_yticklabels(ax.get_yticklabels(), fontsize = 15)
_ = ax.set_xlabel('Passage', fontsize = 20)
_ = ax.set_xticklabels(ax.get_xticklabels(), fontsize = 15)


fig, ax = plt.subplots(figsize = (10,4))
fitted_df_filtered['split'] = fitted_df_filtered['split'].astype('int')
sns.lineplot(data = fitted_df_filtered, x = 'split', y = 'rate', hue = 'Line',
              ax = ax, palette=line_palette)
_ = ax.set_ylabel('Growth rate', fontsize = 20)
_ = ax.set_yticklabels(ax.get_yticklabels(), fontsize = 15)
_ = ax.set_xlabel('Passage', fontsize = 20)
_ = ax.set_xticklabels(ax.get_xticklabels(), fontsize = 15)
ax.legend(bbox_to_anchor = (1,1))

<matplotlib.legend.Legend at 0x7f22c57028f0>


fig, ax = plt.subplots()
sns.boxplot(data = fitted_df_filtered, x = 'Line', y = 'rate', ax = ax, palette=line_palette)
_ = ax.set_ylabel('Rate of area growth', fontsize = 20)
_ = ax.set_yticklabels(ax.get_yticklabels(), fontsize = 15)
_ = ax.set_xlabel('Passage', fontsize = 20)
_ = ax.set_xticklabels(ax.get_xticklabels(), fontsize = 15, rotation = 90)


fitted_df_filtered.sort_values(by = 'rate').to_csv('../../data/csv/iPSC_fitted_exp_area_sum_per_split.csv')


fitted_df_filtered.sort_values(by = 'rate')


pd.DataFrame(fitted_df.groupby('Line')['rate'].mean().sort_values())


data_tot = []

for line_n in fitted_df.index:
    sub = fitted_df.loc[line_n]
    data = pd.DataFrame({'mean_cum': sub['mean_cum'], 'split_time': sub['split_time'], 'line_n': [line_n] * len(sub['mean_cum'])})
    data_tot.append(data)

data_tot = pd.concat(data_tot)

data_tot['Line'] = data_tot['line_n'].apply(lambda x: x.split('_')[0]).values
data_tot['split'] = data_tot['line_n'].apply(lambda x: x.split('_')[1]).values


fig, ax = plt.subplots(4,3, figsize = (20, 20))
ax = ax.flatten().T
ax_index = 0
fitted_param_line = {}

for line in order:

    fitted_param_line[line] = {}
    sub = data_tot[data_tot.Line == line]
    sub = sub.sort_values('split_time')
    
    if len(sub.split_time.unique()) > 5:
        # Calculate the cumulative sum of the mean total area for each split_time
        y = sub['mean_cum'].cumsum().values
        x = np.array(sub.split_time)

        try:
            popt, pcov = curve_fit(exp_model, x, y, p0=(max(y), 0.1, 0))

            fitted_param_line[line]['intercept'] = popt[0]
            fitted_param_line[line]['rate'] = popt[1]
            fitted_param_line[line]['mean_cum'] = y
            fitted_param_line[line]['split_time'] = x
            
            a, b, c = popt
            
            # Generate fitted y values
            y_fitted = exp_model(x, a, b, c)

            fitted_param_line[line]['y_fitted'] = y_fitted
            #fitted_param_line[line]['MSLE'] = mean_squared_log_error(y, y_fitted)
            fitted_param_line[line]['r2'] = r2_score(y, y_fitted)
            
            sns.lineplot(y = y_fitted, x = x, ax = ax[ax_index], markers = True)
            sns.scatterplot(y = y, x = x, ax = ax[ax_index], markers = True)
            ax[ax_index].xaxis.set_tick_params(rotation=90)
            ax[ax_index].set_title(f'{line}', fontsize = 30)
            ax[ax_index].set_ylabel('Increase in area ', fontsize = 20)
            ax[ax_index].set_xlabel('Time point', fontsize = 20)
            _ = ax[ax_index].set_xticklabels(ax[ax_index].get_xticklabels(), fontsize = 15)
            _ = ax[ax_index].set_yticklabels(ax[ax_index].get_yticklabels(), fontsize = 15)
            ax_index += 1
        
        except RuntimeError as e:
            print(f"Fitting failed for line_n {line_n}: {e}")
            
plt.tight_layout()

Fitting failed for line_n UCSFi001-A_5: Optimal parameters not found: Number of calls to function has reached maxfev = 800.


fitted_df_line = pd.DataFrame.from_dict(fitted_param_line).T
fitted_df_line = fitted_df_line[~fitted_df_line.intercept.isna()]
fitted_df_line.sort_values('rate')


fig, ax = plt.subplots(10,5, figsize = (5*10, 7*9), gridspec_kw={'hspace': 0.7})
ax = ax.flatten().T
ax_index = 0

for l in total_df.line.unique():
    
    #color = color_dict[l]

    sub = total_df[total_df.line == l]
    sub = sub.sort_values(by = 'datetime')
    sub = sub[sub.n_split != 'day']

    for split in sub.n_split.unique():

        subsub = sub[sub.n_split == split]

        
        # user defined function,
        # with `area_sum` return the sum of the areas of all the field of view captured for that line at that time point
        summary_df = preprocess(subsub, original_v='Area (microm2)', final_output='mean')
        
        summary_df = summary_df.sort_values('split_time')
        
        summary_df['smoothed'] = gaussian_filter1d(summary_df['mean'], 3)
        summary_df['derivative'] = summary_df['smoothed'].diff() / summary_df['split_time'].diff()
        summary_df['cumulative'] = summary_df['derivative'].cumsum()
        
        farray = np.array(summary_df['cumulative'])
        
        y_pos = summary_df.split_time.values
        
        ax[ax_index].errorbar(y_pos, farray, marker = 'o')

        ax[ax_index].xaxis.set_tick_params(rotation=90)
        ax[ax_index].set_title(f'{l}_split_{split}')
        ax[ax_index].set_ylabel('Cumulative growth')
        ax[ax_index].set_xlabel('Time point')
        ax_index += 1
        
plt.show()


total_df.columns

Index(['total_area', 'perc_area', 'mean_area_per_colony', 'n_colonies',
       'time_point', 'confluency/generation', 'hour', 'month', 'day', 'line',
       'datetime', 'norm_factor', 'perc_area_norm', 'err_bar_mean', 'n_split',
       'split_time', 'pixel_size', 'Area (microm2)', 'Area (mm2)',
       'line_split', 'mean_area_tp', 'area_error', 'std', 'cv', 'line_n_split',
       'logArea'],
      dtype='object')


sns.set(style="white", palette="Paired", color_codes=True)
fig, ax = plt.subplots(figsize=(7,5))
labels = []
lc = []
handles = []
all_lines = {}

total_df_no_first = total_df[total_df.n_split != '1'].copy()

cumulative_dict_dfs = {}

for l in total_df.line.unique():
    
    color = line_palette[l]

    sub = total_df[total_df.line == l]
    sub = sub.sort_values(by = 'datetime')
    sub = sub[sub.n_split != 'day']

    for split in sub.n_split.unique():


        subsub = sub[sub.n_split == split]

        if len(subsub.split_time.unique()) > 0:
    
            # user defined function,
            # with `area_sum` return the sum of the areas of all the field of view captured for that line at that time point
            summary_df = preprocess(subsub, original_v='Area (microm2)', final_output='mean')
            
            summary_df = summary_df.sort_values('split_time')
            
            summary_df['smoothed'] = gaussian_filter1d(summary_df['mean'], 3)
            summary_df['derivative'] = summary_df['smoothed'].diff() / summary_df['split_time'].diff()
            summary_df['cumulative'] = summary_df['derivative'].cumsum()
    
            cumulative_dict_dfs[f'{l}_{split}'] = summary_df
            
            farray = np.array(summary_df['cumulative'])
            
            y_pos = summary_df.split_time.values
    
            line, = ax.plot(y_pos, farray, color = color, marker = '.')
            ax.xaxis.set_tick_params(rotation=90)
            all_lines[f'{l}_{split}'] = line
    
            labels.append(l)
            handles.append(line)
            lc.append(color)
    
plt.legend(handles, labels, bbox_to_anchor = (1,1))

<matplotlib.legend.Legend at 0x7f22b99145e0>


lines = total_df.line.unique()


highlight_growth_curves(all_lines, 
                        xlabel = 'Hours from split', 
                        ylabel = 'Cumulative growth', 
                        lines = lines, fontsize = 20)
plt.savefig('./figures/cumulative_growth_curve_per_line.svg', dpi = 300)


cumulative_df = pd.concat(cumulative_dict_dfs.values(), keys = cumulative_dict_dfs.keys()).reset_index()
cumulative_df['line'] = cumulative_df.level_0.apply(lambda x: x.split('_')[0])


cumulative_df


# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(cumulative_df.sort_values(by = 'split_time'), col="line", palette=line_palette,
                     col_wrap=4, height=5)

grid.map(sns.regplot, "split_time", "cumulative", order = 1)

# Adjust the arrangement of the plots
grid.fig.tight_layout(w_pad=1)


fitted_model = {}
for l in cumulative_df.line.unique():
    
    sub = cumulative_df[cumulative_df.line == l]
    sub = sub.sort_values(by = 'datetime')

    key = f'{l}'
    fitted_model[key] = {}
    fitted_model[key]['slope'], fitted_model[key]['intercept'], fitted_model[key]['rvalue'], fitted_model[key]['pvalue'], fitted_model[key]['stderr'] = stats.linregress(sub['split_time'], sub['cumulative'].fillna(0))


fitted_model_df = pd.DataFrame.from_dict(fitted_model).T.reset_index()
fitted_model_df['line'] = fitted_model_df['index'].apply(lambda x: x.split('_')[0])
fitted_model_df.sort_values(by = 'slope')


fitted_model_df.rvalue.mean()

0.8744732639082722


fitted_model_df.sort_values(by = 'slope').to_csv('../../data/csv/iPSC_fitted_lm_grouped.csv')

	total_area	perc_area	mean_area_per_colony	n_colonies	time_point	confluency/generation	hour	month	day	line	...	split_time	pixel_size	Area (microm2)	Area (mm2)	line_split	mean_area_tp	area_error	std	cv	line_n_split
CTL04E_3_14_11_1.czi	2532723.0	26.606326	1.266362e+06	2.0	14_11_23_t9	generation	9	11	14	CTL04E	...	159.0	1.38	3495157.74	3495.15774	CTL04E_159.0	3.497834e+06	0.000766	50.791607	0.000015	CTL04E_3
CTL02A_3_14_11_3.czi	1051145.0	11.042308	1.313931e+05	8.0	14_11_23_t9	generation	9	11	14	CTL02A	...	44.0	1.38	1450580.10	1450.58010	CTL02A_44.0	1.548576e+06	0.067556	1859.928320	0.001201	CTL02A_3
CHD8WT_3_14_11_1.czi	1655866.0	17.394919	1.655866e+05	10.0	14_11_23_t9	generation	9	11	14	H9	...	44.0	1.38	2285095.08	2285.09508	H9_44.0	9.615470e+05	-0.579209	25120.598728	0.026125	H9_3
KTD8.2_3_14_11_4.czi	862216.0	9.057603	2.874053e+05	3.0	14_11_23_t9	generation	9	11	14	KTD8.2	...	72.0	1.38	1189858.08	1189.85808	KTD8.2_72.0	7.105711e+05	-0.402810	9096.742330	0.012802	KTD8.2_3
CTL09A_4_14_11_5.czi	165602.0	1.739654	2.760033e+04	6.0	14_11_23_t9	generation	9	11	14	CTL09A	...	24.0	1.38	228530.76	228.53076	CTL09A_24.0	2.885001e+05	0.262413	1138.202417	0.003945	CTL09A_4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
CTL08A_5_22_11_2.czi	946317.0	9.941087	6.759407e+04	14.0	22_11_23_t10	generation	10	11	22	CTL08A	...	17.0	1.38	1305917.46	1305.91746	CTL08A_17.0	1.797598e+06	0.376502	9331.962580	0.005191	CTL08A_5
CTL08A_5_22_11_3.czi	638843.0	6.711064	1.064738e+05	6.0	22_11_23_t10	generation	10	11	22	CTL08A	...	17.0	1.38	881603.34	881.60334	CTL08A_17.0	1.797598e+06	1.039010	17385.334447	0.009671	CTL08A_5
CTL01A_5_22_11_4.czi	834622.0	8.767728	5.961586e+04	14.0	22_11_23_t10	generation	10	11	22	CTL01A	...	17.0	1.38	1151778.36	1151.77836	CTL01A_17.0	1.359752e+06	0.180567	3947.276901	0.002903	CTL01A_5
CTL08A_5_22_11_4.czi	857634.0	9.009469	1.072042e+05	8.0	22_11_23_t10	generation	10	11	22	CTL08A	...	17.0	1.38	1183534.92	1183.53492	CTL08A_17.0	1.797598e+06	0.518838	11654.751425	0.006484	CTL08A_5
CHD8WT_5_22_11_1.czi	245495.0	2.578932	1.227475e+05	2.0	22_11_23_t10	generation	10	11	22	H9	...	17.0	1.38	338783.10	338.78310	H9_17.0	1.725624e+06	4.093595	26321.881011	0.015254	H9_5

	a	rate	min(y)	c	mean_cum	split_time	y_fitted	MSLE	r2	Line	split	a+c
CTL01A_2	53.590939	0.031439	48.98586	19.7823	[48.98585999999999, 195.08369999999996, 285.83...	[49.0, 57.0, 72.0, 81.0, 96.0, 105.0, 120.0]	[216.30031656044085, 287.82385334475316, 481.6...	0.377437	0.955038	CTL01A	2	73.373239
CTL01A_4	2552.990753	0.020925	407.73135	357.00324	[407.73135, 650.817798, 1470.892803, 2865.8591...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0]	[357.00324, 822.2200679524135, 2111.6218893054...	0.038346	0.958803	CTL01A	4	2909.993993
CTL02A_1	516.84067	0.02225	405.32601	209.46054	[405.32601, 534.56623, 880.29901, 1318.17853, ...	[39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0, 12...	[923.4798879738361, 1196.3659463168015, 1792.1...	0.202706	0.974893	CTL02A	1	726.30121
CTL02A_2	363.236713	0.043378	481.002864	291.49878	[481.00286399999993, 682.5998189999999, 1748.9...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96.0]	[624.5182863894054, 957.028292531812, 1900.212...	0.030689	0.992677	CTL02A	2	654.735493
CTL02A_3	6321.707636	0.016347	627.73808	249.83244	[627.73808, 992.027444, 1508.292068, 2111.0835...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[249.83244000000195, 788.2321280457018, 2694.5...	0.180534	0.975446	CTL02A	3	6571.540076
CTL02A_5	259.642996	0.037966	248.92647	223.89396	[248.92647, 348.61422, 667.6209999999999, 927....	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[223.89396000008873, 440.8882405407524, 610.04...	0.019593	0.983085	CTL02A	5	483.536956
CTL04E_2	312.465185	0.024744	114.00318	114.00318	[114.00317999999999, 419.92985999999996, 648.0...	[24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105...	[367.40032074991313, 508.5498721900076, 851.96...	0.170421	0.985605	CTL04E	2	426.468365
CTL04E_3	497.069779	0.023063	807.9486	807.9486	[807.9485999999999, 925.2285899999999, 1018.98...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96....	[1013.3974633872776, 1175.452778497335, 1532.7...	0.102829	0.976291	CTL04E	3	1305.018379
CTL04E_4	3657.729993	0.017852	463.8341	327.13452	[463.8341, 808.1382349999999, 1096.15610299999...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0, 81.0,...	[327.13452000000007, 888.649780863635, 2384.63...	0.072583	0.990038	CTL04E	4	3984.864513
CTL04E_5	1127.103565	0.037623	630.44932	320.99214	[630.4493200000001, 855.2126800000001, 968.046...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[320.9921400000087, 1251.6365258470114, 1974.2...	0.327523	0.835627	CTL04E	5	1448.095705
CTL05A_1	316.567038	0.030813	296.488308	189.37602	[296.48830799999996, 923.25726, 1722.042108, 2...	[39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0, 12...	[925.6609348972702, 1262.1413093635813, 2078.4...	0.143865	0.99813	CTL05A	1	505.943058
CTL05A_2	21.167943	0.043012	78.093855	186.873482	[78.09385499999999, 193.287147, 241.072407, 38...	[9.0, 24.0, 33.0, 48.0, 59.0, 72.0, 81.0, 96.0...	[196.87989118736408, 225.13412776210168, 253.2...	0.075352	0.999672	CTL05A	2	208.041424
CTL05A_3	967.663799	0.01896	544.203345	331.29384	[544.2033449999999, 640.3876889999999, 671.980...	[0.0, 15.0, 23.0, 39.0, 47.0, 64.0, 72.0, 87.0...	[331.293840000807, 649.616394634156, 860.24249...	0.123883	0.984739	CTL05A	3	1298.957639
CTL06F_1	73.156567	0.038794	71.0148	56.74836	[71.0148, 191.69752499999998, 376.873929, 754....	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111....	[56.74835999999999, 315.73823501966706, 454.52...	0.035807	0.998507	CTL06F	1	129.904927
CTL06F_2	68.093768	0.048632	84.18644	229.702436	[84.18644, 202.243508, 373.50316399999997, 681...	[0.0, 9.0, 24.0, 33.0, 48.0, 59.0, 72.0, 81.0,...	[229.7024356845842, 267.094655263772, 380.3884...	0.120596	0.998732	CTL06F	2	297.796204
CTL06F_3	3463.1939	0.017721	317.817864	222.41874	[317.817864, 537.075576, 993.100164, 1955.1224...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[222.41874000000007, 543.2838035123386, 1695.5...	0.0539	0.9908	CTL06F	3	3685.61264
CTL06F_5	577.234655	0.028446	378.69431	411.150264	[378.69431000000003, 785.843438, 1021.701998, ...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[411.1502639146694, 743.8635617830383, 976.395...	0.003022	0.996178	CTL06F	5	988.384919
CTL07C_2	94.048429	0.031126	37.458996	14.91918	[37.458996, 104.79609599999999, 185.987292, 28...	[9.0, 24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0...	[45.32533050619237, 119.37803266330768, 183.55...	0.015759	0.990575	CTL07C	2	108.967609
CTL07C_3	613.974584	0.035564	913.37922	784.329447	[913.3792199999999, 1250.2052039999999, 1652.8...	[0.0, 15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0]	[784.3294474004915, 1217.076290757244, 1611.93...	0.00529	0.998319	CTL07C	3	1398.304031
CTL07C_4	2306.799129	0.019101	182.26074	155.64468	[182.26073999999997, 304.74484799999993, 570.8...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[155.64467999999988, 386.81439502233434, 1228....	0.196042	0.978949	CTL07C	4	2462.443809
CTL07C_6	1088.712664	0.030355	273.1388	161.54142	[273.1388, 520.0346, 1032.9212599999998, 2528....	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[161.54142000000002, 842.2836199395206, 1328.6...	0.09787	0.982106	CTL07C	6	1250.254084
CTL08A_1	451.677536	0.033521	127.16148	39.47076	[127.16147999999998, 431.94275999999996, 1240....	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0]	[39.470760000000155, 1257.290524072777, 1845.1...	0.335793	0.980946	CTL08A	1	491.148296
CTL08A_2	268.823282	0.033213	62.86314	45.72906	[62.86313999999999, 162.41026799999997, 252.89...	[0.0, 15.0, 24.0, 39.0, 48.0, 63.0, 72.0, 87.0...	[45.72906000000023, 219.31865687265957, 373.45...	0.07015	0.996042	CTL08A	2	314.552342
CTL08A_3	1710.611504	0.021725	140.72619	62.59404	[140.72618999999997, 259.12742999999995, 598.6...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[62.594040000002224, 258.8779884482817, 993.49...	0.258221	0.973682	CTL08A	3	1773.205544
CTL08A_5	11488.018902	0.003436	273.38996	274.289889	[273.38996000000003, 314.30944400000004, 1437....	[0.0, 16.0, 17.0, 24.0, 39.0, 48.0, 63.0]	[274.28988906899576, 923.5431498814814, 965.31...	0.194679	0.886176	CTL08A	5	11762.308791
CTL09A_2	100.26601	0.036516	158.31084	124.0827	[158.31083999999998, 305.1929799999999, 625.27...	[33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105.0, 12...	[358.39217444033267, 623.9322771951977, 827.53...	0.165134	0.9842	CTL09A	2	224.34871
CTL09A_3	1018.919691	0.028298	416.533404	178.572	[416.53340399999996, 845.522964, 1353.60639600...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96....	[717.3621667745616, 1169.182503451074, 2231.79...	0.091367	0.98176	CTL09A	3	1197.491691
CTL09A_4	1424.537893	0.024477	325.85664	325.85664	[325.85663999999997, 564.9259079999999, 1003.4...	[9.0, 24.0, 32.0, 48.0, 56.0, 73.0, 81.0, 96.0...	[676.9175265747774, 1464.5997812217104, 2019.0...	0.19694	0.990837	CTL09A	4	1750.394533
CTL09A_5	245.43844	0.044788	380.20518	522.094091	[380.20518, 567.604212, 948.6578159999999, 174...	[0.0, 8.0, 24.0, 32.0, 47.0, 56.0, 71.0]	[522.0940905069889, 627.8547624096685, 995.732...	0.029391	0.98726	CTL09A	5	767.532531
H1_1	335.579505	0.022767	48.570756	12.51798	[48.570755999999996, 109.48395599999999, 128.7...	[9.0, 24.0, 33.0, 48.0, 57.0, 72.0, 83.0, 96.0...	[88.83137005064896, 256.4993862718479, 388.296...	0.418782	0.98154	H1	1	348.097485
H1_2	1751.44934	0.013332	940.95162	940.95162	[940.9516199999999, 1109.298648, 1189.804168, ...	[0.0, 15.0, 39.0, 47.0, 64.0, 72.0, 87.0, 95.0...	[940.9516200004766, 1328.6959694906852, 2135.3...	0.161432	0.957078	H1	2	2692.40096
H9_1	1628.27244	0.024254	143.7201	133.4598	[143.72009999999997, 367.2745799999999, 947.59...	[0.0, 15.0, 24.0, 40.0, 48.0, 63.0, 72.0, 87.0...	[133.45980000082363, 847.9387957534793, 1419.4...	0.102503	0.986148	H9	1	1761.73224
H9_2	250.804206	0.045415	114.8643	58.4637	[114.86429999999999, 704.2667159999999, 1200.7...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0]	[303.32447520829385, 553.5933001556893, 1281.8...	0.142488	0.999084	H9	2	309.267906
H9_3	4369.765458	0.015581	344.70882	114.95124	[344.70881999999995, 429.92740799999996, 542.4...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[114.95124000000305, 468.9929864609403, 1712.7...	0.344004	0.975495	H9	3	4484.716698
H9_5	481.000261	0.023808	308.090865	286.856808	[308.090865, 343.77042, 682.5535199999999, 935...	[0.0, 16.0, 17.0, 39.0, 48.0, 63.0]	[286.856807514086, 509.8663602418136, 526.8283...	0.03998	0.965619	H9	5	767.857069
KTD8.2_1	90.861505	0.041092	209.4633	209.4633	[209.46329999999998, 355.660845, 580.268264999...	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0]	[209.46330000000057, 569.8131332224608, 771.72...	0.04646	0.986486	KTD8.2	1	300.324805
KTD8.2_2	143.611111	0.039801	271.62816	667.25949	[271.62816, 429.56363999999996, 1188.767879999...	[0.0, 15.0, 24.0, 39.0, 48.0, 63.0, 72.0, 87.0...	[667.2594903693514, 784.5436633996587, 896.927...	0.133148	0.987279	KTD8.2	2	810.870602
KTD8.2_3	786.554401	0.018607	40.886088	21.48798	[40.886087999999994, 148.963548, 230.079948, 3...	[9.0, 28.0, 33.0, 48.0, 57.0, 72.0, 80.0, 96.0...	[164.8807600222509, 559.2701541222905, 688.395...	0.490549	0.988663	KTD8.2	3	808.042381
KTD8.2_4	642.619191	0.026211	539.310133	512.57997	[539.3101333333333, 824.4388333333333, 1098.55...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[512.5799696858405, 847.3969548514299, 1075.42...	0.003678	0.987125	KTD8.2	4	1155.19916
UCSFi001-A_2	223.991762	0.026581	179.12193	175.46148	[179.12193, 422.4456, 664.010115, 768.59238, 1...	[24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105...	[375.3944790902663, 489.9689160250534, 775.398...	0.097114	0.970674	UCSFi001-A	2	399.453242
UCSFi001-A_3	406.117615	0.025661	313.51438	280.08756	[313.51438, 394.810525, 511.56956499999995, 92...	[0.0, 15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0...	[280.0875600000011, 470.7593523383308, 625.805...	0.013113	0.995604	UCSFi001-A	3	686.205175
UCSFi001-A_4	4524.912278	0.016863	250.04634	250.04634	[250.04633999999996, 519.1565519999999, 1278.6...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0, 81.0,...	[250.0463400000417, 903.5736384696875, 2622.76...	0.084851	0.99402	UCSFi001-A	4	4774.958618
UCSFi001-A_5	264.778941	0.055867	675.93918	505.45398	[675.93918, 889.3241639999999, 1171.628004, 18...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[505.4539800000207, 887.9480350639889, 1252.69...	0.036406	0.981117	UCSFi001-A	5	770.232921

	a	rate	min(y)	c	mean_cum	split_time	y_fitted	MSLE	r2	Line	split	a+c
CTL01A_2	53.590939	0.031439	48.98586	19.7823	[48.98585999999999, 195.08369999999996, 285.83...	[49.0, 57.0, 72.0, 81.0, 96.0, 105.0, 120.0]	[216.30031656044085, 287.82385334475316, 481.6...	0.377437	0.955038	CTL01A	2	73.373239
CTL01A_4	2552.990753	0.020925	407.73135	357.00324	[407.73135, 650.817798, 1470.892803, 2865.8591...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0]	[357.00324, 822.2200679524135, 2111.6218893054...	0.038346	0.958803	CTL01A	4	2909.993993
CTL02A_1	516.84067	0.02225	405.32601	209.46054	[405.32601, 534.56623, 880.29901, 1318.17853, ...	[39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0, 12...	[923.4798879738361, 1196.3659463168015, 1792.1...	0.202706	0.974893	CTL02A	1	726.30121
CTL02A_2	363.236713	0.043378	481.002864	291.49878	[481.00286399999993, 682.5998189999999, 1748.9...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96.0]	[624.5182863894054, 957.028292531812, 1900.212...	0.030689	0.992677	CTL02A	2	654.735493
CTL02A_3	6321.707636	0.016347	627.73808	249.83244	[627.73808, 992.027444, 1508.292068, 2111.0835...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[249.83244000000195, 788.2321280457018, 2694.5...	0.180534	0.975446	CTL02A	3	6571.540076
CTL02A_5	259.642996	0.037966	248.92647	223.89396	[248.92647, 348.61422, 667.6209999999999, 927....	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[223.89396000008873, 440.8882405407524, 610.04...	0.019593	0.983085	CTL02A	5	483.536956
CTL04E_2	312.465185	0.024744	114.00318	114.00318	[114.00317999999999, 419.92985999999996, 648.0...	[24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105...	[367.40032074991313, 508.5498721900076, 851.96...	0.170421	0.985605	CTL04E	2	426.468365
CTL04E_3	497.069779	0.023063	807.9486	807.9486	[807.9485999999999, 925.2285899999999, 1018.98...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96....	[1013.3974633872776, 1175.452778497335, 1532.7...	0.102829	0.976291	CTL04E	3	1305.018379
CTL04E_4	3657.729993	0.017852	463.8341	327.13452	[463.8341, 808.1382349999999, 1096.15610299999...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0, 81.0,...	[327.13452000000007, 888.649780863635, 2384.63...	0.072583	0.990038	CTL04E	4	3984.864513
CTL05A_1	316.567038	0.030813	296.488308	189.37602	[296.48830799999996, 923.25726, 1722.042108, 2...	[39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0, 12...	[925.6609348972702, 1262.1413093635813, 2078.4...	0.143865	0.99813	CTL05A	1	505.943058
CTL05A_2	21.167943	0.043012	78.093855	186.873482	[78.09385499999999, 193.287147, 241.072407, 38...	[9.0, 24.0, 33.0, 48.0, 59.0, 72.0, 81.0, 96.0...	[196.87989118736408, 225.13412776210168, 253.2...	0.075352	0.999672	CTL05A	2	208.041424
CTL05A_3	967.663799	0.01896	544.203345	331.29384	[544.2033449999999, 640.3876889999999, 671.980...	[0.0, 15.0, 23.0, 39.0, 47.0, 64.0, 72.0, 87.0...	[331.293840000807, 649.616394634156, 860.24249...	0.123883	0.984739	CTL05A	3	1298.957639
CTL06F_1	73.156567	0.038794	71.0148	56.74836	[71.0148, 191.69752499999998, 376.873929, 754....	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111....	[56.74835999999999, 315.73823501966706, 454.52...	0.035807	0.998507	CTL06F	1	129.904927
CTL06F_2	68.093768	0.048632	84.18644	229.702436	[84.18644, 202.243508, 373.50316399999997, 681...	[0.0, 9.0, 24.0, 33.0, 48.0, 59.0, 72.0, 81.0,...	[229.7024356845842, 267.094655263772, 380.3884...	0.120596	0.998732	CTL06F	2	297.796204
CTL06F_3	3463.1939	0.017721	317.817864	222.41874	[317.817864, 537.075576, 993.100164, 1955.1224...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[222.41874000000007, 543.2838035123386, 1695.5...	0.0539	0.9908	CTL06F	3	3685.61264
CTL06F_5	577.234655	0.028446	378.69431	411.150264	[378.69431000000003, 785.843438, 1021.701998, ...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[411.1502639146694, 743.8635617830383, 976.395...	0.003022	0.996178	CTL06F	5	988.384919
CTL07C_2	94.048429	0.031126	37.458996	14.91918	[37.458996, 104.79609599999999, 185.987292, 28...	[9.0, 24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0...	[45.32533050619237, 119.37803266330768, 183.55...	0.015759	0.990575	CTL07C	2	108.967609
CTL07C_3	613.974584	0.035564	913.37922	784.329447	[913.3792199999999, 1250.2052039999999, 1652.8...	[0.0, 15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0]	[784.3294474004915, 1217.076290757244, 1611.93...	0.00529	0.998319	CTL07C	3	1398.304031
CTL07C_4	2306.799129	0.019101	182.26074	155.64468	[182.26073999999997, 304.74484799999993, 570.8...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[155.64467999999988, 386.81439502233434, 1228....	0.196042	0.978949	CTL07C	4	2462.443809
CTL07C_6	1088.712664	0.030355	273.1388	161.54142	[273.1388, 520.0346, 1032.9212599999998, 2528....	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[161.54142000000002, 842.2836199395206, 1328.6...	0.09787	0.982106	CTL07C	6	1250.254084
CTL08A_1	451.677536	0.033521	127.16148	39.47076	[127.16147999999998, 431.94275999999996, 1240....	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0]	[39.470760000000155, 1257.290524072777, 1845.1...	0.335793	0.980946	CTL08A	1	491.148296
CTL08A_2	268.823282	0.033213	62.86314	45.72906	[62.86313999999999, 162.41026799999997, 252.89...	[0.0, 15.0, 24.0, 39.0, 48.0, 63.0, 72.0, 87.0...	[45.72906000000023, 219.31865687265957, 373.45...	0.07015	0.996042	CTL08A	2	314.552342
CTL08A_3	1710.611504	0.021725	140.72619	62.59404	[140.72618999999997, 259.12742999999995, 598.6...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[62.594040000002224, 258.8779884482817, 993.49...	0.258221	0.973682	CTL08A	3	1773.205544
CTL09A_2	100.26601	0.036516	158.31084	124.0827	[158.31083999999998, 305.1929799999999, 625.27...	[33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105.0, 12...	[358.39217444033267, 623.9322771951977, 827.53...	0.165134	0.9842	CTL09A	2	224.34871
CTL09A_3	1018.919691	0.028298	416.533404	178.572	[416.53340399999996, 845.522964, 1353.60639600...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96....	[717.3621667745616, 1169.182503451074, 2231.79...	0.091367	0.98176	CTL09A	3	1197.491691
CTL09A_4	1424.537893	0.024477	325.85664	325.85664	[325.85663999999997, 564.9259079999999, 1003.4...	[9.0, 24.0, 32.0, 48.0, 56.0, 73.0, 81.0, 96.0...	[676.9175265747774, 1464.5997812217104, 2019.0...	0.19694	0.990837	CTL09A	4	1750.394533
CTL09A_5	245.43844	0.044788	380.20518	522.094091	[380.20518, 567.604212, 948.6578159999999, 174...	[0.0, 8.0, 24.0, 32.0, 47.0, 56.0, 71.0]	[522.0940905069889, 627.8547624096685, 995.732...	0.029391	0.98726	CTL09A	5	767.532531
H1_1	335.579505	0.022767	48.570756	12.51798	[48.570755999999996, 109.48395599999999, 128.7...	[9.0, 24.0, 33.0, 48.0, 57.0, 72.0, 83.0, 96.0...	[88.83137005064896, 256.4993862718479, 388.296...	0.418782	0.98154	H1	1	348.097485
H1_2	1751.44934	0.013332	940.95162	940.95162	[940.9516199999999, 1109.298648, 1189.804168, ...	[0.0, 15.0, 39.0, 47.0, 64.0, 72.0, 87.0, 95.0...	[940.9516200004766, 1328.6959694906852, 2135.3...	0.161432	0.957078	H1	2	2692.40096
H9_1	1628.27244	0.024254	143.7201	133.4598	[143.72009999999997, 367.2745799999999, 947.59...	[0.0, 15.0, 24.0, 40.0, 48.0, 63.0, 72.0, 87.0...	[133.45980000082363, 847.9387957534793, 1419.4...	0.102503	0.986148	H9	1	1761.73224
H9_2	250.804206	0.045415	114.8643	58.4637	[114.86429999999999, 704.2667159999999, 1200.7...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0]	[303.32447520829385, 553.5933001556893, 1281.8...	0.142488	0.999084	H9	2	309.267906
H9_3	4369.765458	0.015581	344.70882	114.95124	[344.70881999999995, 429.92740799999996, 542.4...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[114.95124000000305, 468.9929864609403, 1712.7...	0.344004	0.975495	H9	3	4484.716698
H9_5	481.000261	0.023808	308.090865	286.856808	[308.090865, 343.77042, 682.5535199999999, 935...	[0.0, 16.0, 17.0, 39.0, 48.0, 63.0]	[286.856807514086, 509.8663602418136, 526.8283...	0.03998	0.965619	H9	5	767.857069
KTD8.2_1	90.861505	0.041092	209.4633	209.4633	[209.46329999999998, 355.660845, 580.268264999...	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0]	[209.46330000000057, 569.8131332224608, 771.72...	0.04646	0.986486	KTD8.2	1	300.324805
KTD8.2_2	143.611111	0.039801	271.62816	667.25949	[271.62816, 429.56363999999996, 1188.767879999...	[0.0, 15.0, 24.0, 39.0, 48.0, 63.0, 72.0, 87.0...	[667.2594903693514, 784.5436633996587, 896.927...	0.133148	0.987279	KTD8.2	2	810.870602
KTD8.2_3	786.554401	0.018607	40.886088	21.48798	[40.886087999999994, 148.963548, 230.079948, 3...	[9.0, 28.0, 33.0, 48.0, 57.0, 72.0, 80.0, 96.0...	[164.8807600222509, 559.2701541222905, 688.395...	0.490549	0.988663	KTD8.2	3	808.042381
KTD8.2_4	642.619191	0.026211	539.310133	512.57997	[539.3101333333333, 824.4388333333333, 1098.55...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[512.5799696858405, 847.3969548514299, 1075.42...	0.003678	0.987125	KTD8.2	4	1155.19916
UCSFi001-A_2	223.991762	0.026581	179.12193	175.46148	[179.12193, 422.4456, 664.010115, 768.59238, 1...	[24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105...	[375.3944790902663, 489.9689160250534, 775.398...	0.097114	0.970674	UCSFi001-A	2	399.453242
UCSFi001-A_3	406.117615	0.025661	313.51438	280.08756	[313.51438, 394.810525, 511.56956499999995, 92...	[0.0, 15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0...	[280.0875600000011, 470.7593523383308, 625.805...	0.013113	0.995604	UCSFi001-A	3	686.205175
UCSFi001-A_4	4524.912278	0.016863	250.04634	250.04634	[250.04633999999996, 519.1565519999999, 1278.6...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0, 81.0,...	[250.0463400000417, 903.5736384696875, 2622.76...	0.084851	0.99402	UCSFi001-A	4	4774.958618
UCSFi001-A_5	264.778941	0.055867	675.93918	505.45398	[675.93918, 889.3241639999999, 1171.628004, 18...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[505.4539800000207, 887.9480350639889, 1252.69...	0.036406	0.981117	UCSFi001-A	5	770.232921

	a	rate	min(y)	c	mean_cum	split_time	y_fitted	MSLE	r2	Line	split	a+c
H1_2	1751.44934	0.013332	940.95162	940.95162	[940.9516199999999, 1109.298648, 1189.804168, ...	[0.0, 15.0, 39.0, 47.0, 64.0, 72.0, 87.0, 95.0...	[940.9516200004766, 1328.6959694906852, 2135.3...	0.161432	0.957078	H1	2	2692.40096
H9_3	4369.765458	0.015581	344.70882	114.95124	[344.70881999999995, 429.92740799999996, 542.4...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[114.95124000000305, 468.9929864609403, 1712.7...	0.344004	0.975495	H9	3	4484.716698
CTL02A_3	6321.707636	0.016347	627.73808	249.83244	[627.73808, 992.027444, 1508.292068, 2111.0835...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[249.83244000000195, 788.2321280457018, 2694.5...	0.180534	0.975446	CTL02A	3	6571.540076
UCSFi001-A_4	4524.912278	0.016863	250.04634	250.04634	[250.04633999999996, 519.1565519999999, 1278.6...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0, 81.0,...	[250.0463400000417, 903.5736384696875, 2622.76...	0.084851	0.99402	UCSFi001-A	4	4774.958618
CTL06F_3	3463.1939	0.017721	317.817864	222.41874	[317.817864, 537.075576, 993.100164, 1955.1224...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[222.41874000000007, 543.2838035123386, 1695.5...	0.0539	0.9908	CTL06F	3	3685.61264
CTL04E_4	3657.729993	0.017852	463.8341	327.13452	[463.8341, 808.1382349999999, 1096.15610299999...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0, 81.0,...	[327.13452000000007, 888.649780863635, 2384.63...	0.072583	0.990038	CTL04E	4	3984.864513
KTD8.2_3	786.554401	0.018607	40.886088	21.48798	[40.886087999999994, 148.963548, 230.079948, 3...	[9.0, 28.0, 33.0, 48.0, 57.0, 72.0, 80.0, 96.0...	[164.8807600222509, 559.2701541222905, 688.395...	0.490549	0.988663	KTD8.2	3	808.042381
CTL05A_3	967.663799	0.01896	544.203345	331.29384	[544.2033449999999, 640.3876889999999, 671.980...	[0.0, 15.0, 23.0, 39.0, 47.0, 64.0, 72.0, 87.0...	[331.293840000807, 649.616394634156, 860.24249...	0.123883	0.984739	CTL05A	3	1298.957639
CTL07C_4	2306.799129	0.019101	182.26074	155.64468	[182.26073999999997, 304.74484799999993, 570.8...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[155.64467999999988, 386.81439502233434, 1228....	0.196042	0.978949	CTL07C	4	2462.443809
CTL01A_4	2552.990753	0.020925	407.73135	357.00324	[407.73135, 650.817798, 1470.892803, 2865.8591...	[0.0, 8.0, 25.0, 33.0, 48.0, 56.0, 73.0]	[357.00324, 822.2200679524135, 2111.6218893054...	0.038346	0.958803	CTL01A	4	2909.993993
CTL08A_3	1710.611504	0.021725	140.72619	62.59404	[140.72618999999997, 259.12742999999995, 598.6...	[0.0, 5.0, 20.0, 29.0, 44.0, 52.0, 68.0, 76.0,...	[62.594040000002224, 258.8779884482817, 993.49...	0.258221	0.973682	CTL08A	3	1773.205544
CTL02A_1	516.84067	0.02225	405.32601	209.46054	[405.32601, 534.56623, 880.29901, 1318.17853, ...	[39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0, 12...	[923.4798879738361, 1196.3659463168015, 1792.1...	0.202706	0.974893	CTL02A	1	726.30121
H1_1	335.579505	0.022767	48.570756	12.51798	[48.570755999999996, 109.48395599999999, 128.7...	[9.0, 24.0, 33.0, 48.0, 57.0, 72.0, 83.0, 96.0...	[88.83137005064896, 256.4993862718479, 388.296...	0.418782	0.98154	H1	1	348.097485
CTL04E_3	497.069779	0.023063	807.9486	807.9486	[807.9485999999999, 925.2285899999999, 1018.98...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96....	[1013.3974633872776, 1175.452778497335, 1532.7...	0.102829	0.976291	CTL04E	3	1305.018379
H9_5	481.000261	0.023808	308.090865	286.856808	[308.090865, 343.77042, 682.5535199999999, 935...	[0.0, 16.0, 17.0, 39.0, 48.0, 63.0]	[286.856807514086, 509.8663602418136, 526.8283...	0.03998	0.965619	H9	5	767.857069
H9_1	1628.27244	0.024254	143.7201	133.4598	[143.72009999999997, 367.2745799999999, 947.59...	[0.0, 15.0, 24.0, 40.0, 48.0, 63.0, 72.0, 87.0...	[133.45980000082363, 847.9387957534793, 1419.4...	0.102503	0.986148	H9	1	1761.73224
CTL09A_4	1424.537893	0.024477	325.85664	325.85664	[325.85663999999997, 564.9259079999999, 1003.4...	[9.0, 24.0, 32.0, 48.0, 56.0, 73.0, 81.0, 96.0...	[676.9175265747774, 1464.5997812217104, 2019.0...	0.19694	0.990837	CTL09A	4	1750.394533
CTL04E_2	312.465185	0.024744	114.00318	114.00318	[114.00317999999999, 419.92985999999996, 648.0...	[24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105...	[367.40032074991313, 508.5498721900076, 851.96...	0.170421	0.985605	CTL04E	2	426.468365
UCSFi001-A_3	406.117615	0.025661	313.51438	280.08756	[313.51438, 394.810525, 511.56956499999995, 92...	[0.0, 15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0...	[280.0875600000011, 470.7593523383308, 625.805...	0.013113	0.995604	UCSFi001-A	3	686.205175
KTD8.2_4	642.619191	0.026211	539.310133	512.57997	[539.3101333333333, 824.4388333333333, 1098.55...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[512.5799696858405, 847.3969548514299, 1075.42...	0.003678	0.987125	KTD8.2	4	1155.19916
UCSFi001-A_2	223.991762	0.026581	179.12193	175.46148	[179.12193, 422.4456, 664.010115, 768.59238, 1...	[24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105...	[375.3944790902663, 489.9689160250534, 775.398...	0.097114	0.970674	UCSFi001-A	2	399.453242
CTL09A_3	1018.919691	0.028298	416.533404	178.572	[416.53340399999996, 845.522964, 1353.60639600...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96....	[717.3621667745616, 1169.182503451074, 2231.79...	0.091367	0.98176	CTL09A	3	1197.491691
CTL06F_5	577.234655	0.028446	378.69431	411.150264	[378.69431000000003, 785.843438, 1021.701998, ...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[411.1502639146694, 743.8635617830383, 976.395...	0.003022	0.996178	CTL06F	5	988.384919
CTL07C_6	1088.712664	0.030355	273.1388	161.54142	[273.1388, 520.0346, 1032.9212599999998, 2528....	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[161.54142000000002, 842.2836199395206, 1328.6...	0.09787	0.982106	CTL07C	6	1250.254084
CTL05A_1	316.567038	0.030813	296.488308	189.37602	[296.48830799999996, 923.25726, 1722.042108, 2...	[39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0, 12...	[925.6609348972702, 1262.1413093635813, 2078.4...	0.143865	0.99813	CTL05A	1	505.943058
CTL07C_2	94.048429	0.031126	37.458996	14.91918	[37.458996, 104.79609599999999, 185.987292, 28...	[9.0, 24.0, 33.0, 49.0, 57.0, 72.0, 81.0, 96.0...	[45.32533050619237, 119.37803266330768, 183.55...	0.015759	0.990575	CTL07C	2	108.967609
CTL01A_2	53.590939	0.031439	48.98586	19.7823	[48.98585999999999, 195.08369999999996, 285.83...	[49.0, 57.0, 72.0, 81.0, 96.0, 105.0, 120.0]	[216.30031656044085, 287.82385334475316, 481.6...	0.377437	0.955038	CTL01A	2	73.373239
CTL08A_2	268.823282	0.033213	62.86314	45.72906	[62.86313999999999, 162.41026799999997, 252.89...	[0.0, 15.0, 24.0, 39.0, 48.0, 63.0, 72.0, 87.0...	[45.72906000000023, 219.31865687265957, 373.45...	0.07015	0.996042	CTL08A	2	314.552342
CTL08A_1	451.677536	0.033521	127.16148	39.47076	[127.16147999999998, 431.94275999999996, 1240....	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0]	[39.470760000000155, 1257.290524072777, 1845.1...	0.335793	0.980946	CTL08A	1	491.148296
CTL07C_3	613.974584	0.035564	913.37922	784.329447	[913.3792199999999, 1250.2052039999999, 1652.8...	[0.0, 15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0]	[784.3294474004915, 1217.076290757244, 1611.93...	0.00529	0.998319	CTL07C	3	1398.304031
CTL09A_2	100.26601	0.036516	158.31084	124.0827	[158.31083999999998, 305.1929799999999, 625.27...	[33.0, 49.0, 57.0, 72.0, 81.0, 96.0, 105.0, 12...	[358.39217444033267, 623.9322771951977, 827.53...	0.165134	0.9842	CTL09A	2	224.34871
CTL02A_5	259.642996	0.037966	248.92647	223.89396	[248.92647, 348.61422, 667.6209999999999, 927....	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[223.89396000008873, 440.8882405407524, 610.04...	0.019593	0.983085	CTL02A	5	483.536956
CTL06F_1	73.156567	0.038794	71.0148	56.74836	[71.0148, 191.69752499999998, 376.873929, 754....	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111....	[56.74835999999999, 315.73823501966706, 454.52...	0.035807	0.998507	CTL06F	1	129.904927
KTD8.2_2	143.611111	0.039801	271.62816	667.25949	[271.62816, 429.56363999999996, 1188.767879999...	[0.0, 15.0, 24.0, 39.0, 48.0, 63.0, 72.0, 87.0...	[667.2594903693514, 784.5436633996587, 896.927...	0.133148	0.987279	KTD8.2	2	810.870602
KTD8.2_1	90.861505	0.041092	209.4633	209.4633	[209.46329999999998, 355.660845, 580.268264999...	[0.0, 39.0, 48.0, 63.0, 72.0, 88.0, 96.0, 111.0]	[209.46330000000057, 569.8131332224608, 771.72...	0.04646	0.986486	KTD8.2	1	300.324805
CTL05A_2	21.167943	0.043012	78.093855	186.873482	[78.09385499999999, 193.287147, 241.072407, 38...	[9.0, 24.0, 33.0, 48.0, 59.0, 72.0, 81.0, 96.0...	[196.87989118736408, 225.13412776210168, 253.2...	0.075352	0.999672	CTL05A	2	208.041424
CTL02A_2	363.236713	0.043378	481.002864	291.49878	[481.00286399999993, 682.5998189999999, 1748.9...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0, 96.0]	[624.5182863894054, 957.028292531812, 1900.212...	0.030689	0.992677	CTL02A	2	654.735493
CTL09A_5	245.43844	0.044788	380.20518	522.094091	[380.20518, 567.604212, 948.6578159999999, 174...	[0.0, 8.0, 24.0, 32.0, 47.0, 56.0, 71.0]	[522.0940905069889, 627.8547624096685, 995.732...	0.029391	0.98726	CTL09A	5	767.532531
H9_2	250.804206	0.045415	114.8643	58.4637	[114.86429999999999, 704.2667159999999, 1200.7...	[15.0, 24.0, 39.0, 50.0, 63.0, 72.0, 87.0]	[303.32447520829385, 553.5933001556893, 1281.8...	0.142488	0.999084	H9	2	309.267906
CTL06F_2	68.093768	0.048632	84.18644	229.702436	[84.18644, 202.243508, 373.50316399999997, 681...	[0.0, 9.0, 24.0, 33.0, 48.0, 59.0, 72.0, 81.0,...	[229.7024356845842, 267.094655263772, 380.3884...	0.120596	0.998732	CTL06F	2	297.796204
UCSFi001-A_5	264.778941	0.055867	675.93918	505.45398	[675.93918, 889.3241639999999, 1171.628004, 18...	[0.0, 16.0, 24.0, 39.0, 48.0, 63.0]	[505.4539800000207, 887.9480350639889, 1252.69...	0.036406	0.981117	UCSFi001-A	5	770.232921

	intercept	rate	mean_cum	split_time	y_fitted	r2
CTL04E	77718.739098	0.009307	[463.8341, 1094.28342, 1902.4216549999999, 271...	[0.0, 0.0, 8.0, 15.0, 16.0, 24.0, 24.0, 24.0, ...	[-13318.63230998673, -13318.63230998673, -7311...	0.977045
KTD8.2	31449.366199	0.010281	[209.46329999999998, 748.7734333333333, 1020.4...	[0.0, 0.0, 0.0, 9.0, 15.0, 16.0, 24.0, 24.0, 2...	[-5888.99421008131, -5888.99421008131, -5888.9...	0.98984
H9	42196.039006	0.013802	[143.72009999999997, 451.810965, 796.519785, 1...	[0.0, 0.0, 0.0, 5.0, 15.0, 15.0, 16.0, 17.0, 2...	[-14202.131016338491, -14202.131016338491, -14...	0.986461
UCSFi001-A	39047.258426	0.013856	[313.51438, 563.56072, 1239.4998999999998, 175...	[0.0, 0.0, 0.0, 8.0, 15.0, 16.0, 24.0, 24.0, 2...	[-10942.223859024449, -10942.223859024449, -10...	0.975132
CTL07C	23591.80031	0.015562	[273.1388, 455.39954, 1368.77876, 1673.523608,...	[0.0, 0.0, 0.0, 5.0, 9.0, 15.0, 16.0, 20.0, 24...	[-3751.0660428077354, -3751.0660428077354, -37...	0.996057
CTL05A	12604.421219	0.017384	[544.2033449999999, 622.2971999999999, 1262.68...	[0.0, 9.0, 15.0, 23.0, 24.0, 33.0, 39.0, 39.0,...	[-12862.94760573278, -10728.29639654801, -9107...	0.95798
CTL02A	26543.595486	0.018447	[627.73808, 876.66455, 1868.6919939999998, 234...	[0.0, 0.0, 5.0, 15.0, 16.0, 20.0, 24.0, 24.0, ...	[-13963.093537789186, -13963.093537789186, -11...	0.990371
CTL09A	19625.227048	0.019058	[380.20518, 947.8093919999999, 1273.6660319999...	[0.0, 8.0, 9.0, 15.0, 24.0, 24.0, 24.0, 32.0, ...	[-11069.602807230889, -7837.397369140426, -739...	0.990286
H1	5121.873312	0.019555	[940.9516199999999, 989.5223759999999, 2098.82...	[0.0, 9.0, 15.0, 24.0, 33.0, 39.0, 47.0, 48.0,...	[-4069.5514137167056, -3083.9292773786065, -23...	0.987598
CTL08A	14967.053698	0.019842	[127.16147999999998, 400.55144, 463.41458, 604...	[0.0, 0.0, 0.0, 0.0, 5.0, 15.0, 16.0, 17.0, 20...	[-8154.354064179996, -8154.354064179996, -8154...	0.984026
CTL06F	14207.486024	0.019961	[71.0148, 449.70911, 533.89555, 851.713414, 13...	[0.0, 0.0, 0.0, 0.0, 5.0, 9.0, 16.0, 20.0, 24....	[-6175.114095213579, -6175.114095213579, -6175...	0.993179

Load data¶

Growth curves total area - by line¶

Growth curves total area - by line and split¶

Growth curves log total area¶

Discrete derivative of the curves - per line per split¶

Discrete derivative of the curves - per line¶

Cumulative of the areas - per line per split¶

Cumulative sum of the discrete derivative of the curves - per line per split¶

Fit linear model¶

	rate
Line
H1	0.01805
CTL08A	0.022974
CTL04E	0.02582
CTL01A	0.026182
H9	0.027264
CTL07C	0.029036
CTL02A	0.029985
CTL05A	0.030928
UCSFi001-A	0.031243
KTD8.2	0.031428
CTL06F	0.033399
CTL09A	0.03352

	level_0	level_1	time_point	mean	stds	hour	month	day	datetime	split_time	smoothed	derivative	cumulative	line
0	H1_1	0	06_11_23_t18	48570.756	30156.872900	18	11	06	2023-11-06 18:00:00	0.0	110143.004205	NaN	NaN	H1
1	H1_1	2	07_11_23_t9	60913.200	94781.769346	9	11	07	2023-11-07 09:00:00	15.0	137570.951804	1828.529840	1828.529840	H1
2	H1_1	1	07_11_23_t18	19238.925	12090.104228	18	11	07	2023-11-07 18:00:00	24.0	195181.480282	6401.169831	8229.699671	H1
3	H1_1	4	08_11_23_t9	72999.585	102548.695788	9	11	08	2023-11-08 09:00:00	39.0	287232.524109	6136.736255	14366.435926	H1
4	H1_1	3	08_11_23_t18	55094.292	61100.627807	18	11	08	2023-11-08 18:00:00	48.0	417456.373402	14469.316588	28835.752514	H1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
459	CTL01A_5	3	20_11_23_t9	188056.464	63817.297072	9	11	20	2023-11-20 09:00:00	23.0	425051.456538	3051.706842	6177.820375	CTL01A
460	CTL01A_5	2	20_11_23_t18	633256.125	453126.246308	18	11	20	2023-11-20 18:00:00	32.0	483465.005972	6490.394382	12668.214756	CTL01A
461	CTL01A_5	5	21_11_23_t9	568493.484	354817.532310	9	11	21	2023-11-21 09:00:00	47.0	543394.794597	3995.319242	16663.533998	CTL01A
462	CTL01A_5	4	21_11_23_t17	386422.080	NaN	17	11	21	2023-11-21 17:00:00	55.0	592580.870876	6148.259535	22811.793533	CTL01A
463	CTL01A_5	6	22_11_23_t10	1151778.360	NaN	10	11	22	2023-11-22 10:00:00	72.0	620324.506463	1631.978564	24443.772097	CTL01A

	index	slope	intercept	rvalue	pvalue	stderr	line
11	CTL01A	644.317934	-2403.923983	0.658753	3.427561e-04	153.439932	CTL01A
0	H1	1092.216426	-17870.728308	0.929554	3.246271e-13	83.369973	H1
1	CTL04E	1957.784502	-21455.175368	0.849852	4.029196e-12	196.950623	CTL04E
3	CTL05A	2002.574201	-24668.872554	0.858594	5.454580e-12	199.285868	CTL05A
5	KTD8.2	2123.269235	-46580.571519	0.961890	3.878081e-24	95.435362	KTD8.2
10	UCSFi001-A	2410.553184	-34882.929624	0.873562	1.020601e-14	207.238467	UCSFi001-A
4	H9	3810.734488	-62725.125730	0.940884	5.551798e-19	225.539637	H9
9	CTL07C	4069.466071	-98092.360468	0.872476	2.224665e-13	369.740303	CTL07C
8	CTL08A	4263.987240	-102963.382905	0.888082	9.120149e-16	340.563762	CTL08A
7	CTL06F	4575.687405	-109378.476161	0.905129	3.365469e-17	331.626382	CTL06F
6	CTL09A	5028.233033	-88363.366680	0.895983	1.307039e-14	409.717545	CTL09A
2	CTL02A	5231.682227	-91740.048754	0.858920	1.340509e-12	506.011614	CTL02A